Moreover, we set the prior distribution \(p(z|x)\)  to uniform in order to push all experts to generate good hypotheses for any sample and alleviate the degeneracy that only one expert gets trained well due to the “rich get richer” effect.
The expert models are trained using the EM algorithm [1]} and the objective function with uniform prior and hard assignments is formulated as:
\(L = \mathbb {E}_{(x,y)\sim D}\big [\min _z -\log p(y|z,x)\big ].\) 
